Joint training of network architecture search and multi-task dense prediction models for edge deployment

A system comprising one or more processors and memory storing instructions that, in response to execution of the instructions, cause the one or more processors to:

• obtain a set of tasks to be performed using a resource-constrained edge computing system;
• based on a base multi-task dense-prediction (MT-DP) architecture template, the set of tasks, and a plurality of hardware-based constraints of the edge computing system, and using a network architecture search (NAS), sample one or more candidate MT-DP architectures from a search space of neural network architecture components, wherein each sampled candidate MT-DP architecture comprises a distinct assembly of sampled neural network architecture components applied to the base MT-DP architecture template;
• and
• process image data using the one or more candidate MT-DP architectures to determine one or more performance metrics for each of the one or more candidate MT-DP architectures.

A method implemented using one or more processors and comprising:

• obtaining a plurality of images capturing crops growing in an agricultural plot;
• processing the plurality of images using one or more candidate multi-task dense-prediction (MT-DP) machine learning models to perform a plurality of agricultural prediction tasks, including one or more agricultural prediction tasks that generate pixel-level predictions for the plurality of images, wherein each of the one or more MT-DP machine learning models was assembled using neural network layers sampled from a search space of neural network layers having different parameters using a network architecture search (NAS);
• and
• operating one or more agricultural vehicles in the agricultural plot based on the pixel-level predictions for the plurality of images.